{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 温度预测的问题\n",
    "\n",
    "数据集包含 14 个维度不同的数据，其中包含气温，这里准备使用其他的维度来执行温度的预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\"Date Time\"', '\"p (mbar)\"', '\"T (degC)\"', '\"Tpot (K)\"', '\"Tdew (degC)\"', '\"rh (%)\"', '\"VPmax (mbar)\"', '\"VPact (mbar)\"', '\"VPdef (mbar)\"', '\"sh (g/kg)\"', '\"H2OC (mmol/mol)\"', '\"rho (g/m**3)\"', '\"wv (m/s)\"', '\"max. wv (m/s)\"', '\"wd (deg)\"']\n",
      "420551\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "data_dir = '/users/haoxingxiao/academic/data/jena_climate'\n",
    "fname = os.path.join(data_dir, 'jena_climate_2009_2016.csv')\n",
    "\n",
    "f = open(fname)\n",
    "data = f.read()\n",
    "f.close()\n",
    "\n",
    "lines = data.split('\\n')\n",
    "header = lines[0].split(',')\n",
    "lines = lines[1:]\n",
    "\n",
    "print(header)\n",
    "print(len(lines))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将 42W 行数据转换成 Numpy 数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128c2fad0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "float_data = np.zeros((len(lines), len(header) - 1))\n",
    "for i, line in enumerate(lines):\n",
    "    values = [float(x) for x in line.split(',')[1:]]\n",
    "    float_data[i, :] = values\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 获取温度\n",
    "temp = float_data[:, 1] \n",
    "plt.plot(range(len(temp)), temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以明显看出温度的周期性变化。\n",
    "每过 10 分钟进行一次记录，所以每天有 144 个数据，看前 10 天的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128c6e6d0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1440), temp[:1440])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备数据：\n",
    "\n",
    "|变量|数值|描述|\n",
    "|--|-|---|\n",
    "|lookback|720|给定过去 5 天的数据|\n",
    "|steps|6|每小时一个数据点|\n",
    "|delay|144|执行预测的是未来 24 小时的数据|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
